1. Volume 95, Issue 3, Pages 697-708.e4 (August 2017)
Partially Mixed Selectivity in Human Posterior Parietal Association Cortex 
Carey Y. Zhang, Tyson Aflalo, Boris Revechkis, Emily R. Rosario, Debra Ouellette, Nader Pouratian, Richard A. Andersen 
Neuron 
Volume 95, Issue 3, Pages e4 (August 2017)
DOI: /j.neuron
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2. Figure 1
Neurons in PPC Exhibit Mixed Selectivity to Movement Variables
(A) Delayed movement paradigm. N.S. was cued as to what kind of movement to perform (e.g., imagine/attempt left/right hand/shoulder) and then cued to perform the movement after a brief delay. See STAR Methods for more details.
(B–E) Single unit example responses over time (mean ± SEM) demonstrating diverse coding to the different conditions.
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3. Figure 2 Significant Tuning to Each Movement Condition
(A) The fraction of units in the population tuned for each condition in the Delay and Go phases, separated by body part and body side (95% confidence interval). A unit was considered tuned to a condition if the beta value of the linear fit for the condition (Linear analyses 1, STAR Methods) was statistically significant (p < 0.05, uncorrected). See also Figure S3 for pairwise comparisons between conditions and Figure S5 for results of individual sessions.
(B) The magnitudes of the units’ tuning to each condition in the Delay and Go phases, as defined by the area under the receiver operating characteristic curve (AUC) between Delay/Go and ITI activity, separated by body parts (95% confidence interval). Only significant AUC values were included in analyses (shuffle test, p < 0.05 uncorrected). See also Figure S4 for the AUC values of excitatory (positively tuned) and inhibitory (negatively tuned) units presented separately, as well as Figure S3 for pairwise comparisons between conditions (Att R, attempt right; Att L, attempt left; Imag R, imagine right; Imag L, imagine left; Spk R, speak right; Spk L, speak left).
(C) Fraction of units with significant tuning to each motor variable and the interaction terms for both the Delay (blue) and Go (red) phases, as opposed to the eight movement conditions in (A) (p < 0.05, uncorrected 95% confidence intervals, see also Linear analysis 2 in STAR Methods).
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4. Figure 3 Possible Organizational Models of Neural Representations
(A) The neurons coding for each condition are anatomically segregated, i.e., distinct, non-overlapping networks (ALH, attempt left hand; ILH, imagine left hand; ARH, attempt right hand; IRH, imagine right hand; ALS, attempt left shoulder; ILS, imagine left shoulder; ARS, attempt right shoulder; IRS, imagine right shoulder).
(B) Conditions can be overlapping such that the responses to some conditions are subsets or weak versions of others, e.g., imagined movements being subsets of attempted movements.
(C) Neurons coding each of the motor variables (body part, body side, and strategy) are anatomically segregated.
(D) The neural population exhibits mixed selectivity, with individual neurons showing tuning to various conjunctions of variables.
(E) The neural population exhibits partially mixed selectivity, with the mixing of representations being dependent on the variables under investigation. Here, hand and shoulder are mixed leading to orthogonal coding of effectors (functional segregation); however, the other variables (body side and strategy) are mixed only within, but not between, effectors. This model is consistent with the results observed in this study. Note that solid lines in this diagram indicate anatomical boundaries of neural populations, while dotted line indicates functional boundaries/segregation.
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5. Figure 4 Specificity of Coding for Motor Variables
Each panel (A)–(F) shows the degree to which neurons code one variable exclusively, its opposite, or respond similarly for both. Only units with significant modulation for at least one condition in the comparison are included in the analyses (p < 0.05, Bonferroni corrected).
(A and B) Distribution of the degree of specificity to the imagine or attempt strategies in the population during trials using different sides, showing only units responsive to one or both strategies.
(C and D) Distribution of the degree of specificity to the left or right side in the population for different strategies.
(E and F) Distribution of the degree of specificity to the hand or shoulder in the population during trials using different sides.
(G and H) Distribution of the degree of specificity to attempted/imagined movements compared to speaking.
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6. Figure 5 Functional Relationships between Movement Conditions
(A) Similarity between population-level neural responses for each movement condition. Pairwise comparisons are separated by the number of motor dimensions that differ in the comparison (left to right) and task phase (movement or delay). Similarity measured as the pairwise correlation between movement conditions (ALH, attempt left hand; ILH, imagine left hand; ARH, attempt right hand; IRH, imagine right hand; ALS, attempt left shoulder; ILS, imagine left shoulder; ARS, attempt right shoulder; IRS, imagine right shoulder). See also Figure S5 for results of individual sessions.
(B) Correlations between four movement types: left and right movements (averaged across both strategies), and speech (SL, speak left; SR, speak right; ML, movement left; MR, movement right).
(C) Dendrogram summarizing the structure apparent in (A), namely strong segregation by effector.
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7. Figure 6 Segregation by Body Part
(A) Average correlation between movement conditions differing by exactly one task variable and grouped by the differing conditions (e.g., for strategy, the average correlation of all movement condition pairs differing only by strategy). Intervals represent the 95% confidence intervals.
(B) For movements above and below the level of injury, average correlation between movement conditions in the Delay and Go phases grouped by the number of differing traits (average of each cube, Figure 5A). Intervals represent the 95% confidence intervals in the correlations.
(C and D) Same as (A) and (B) but with shoulder shrug movements replaced with shoulder abduction movements (a movement below the level of injury).
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8. Figure 7
Representations of Variables Generalize across Side and Strategy but Not Body Part
(A and B) Schematic illustrating expected classifier behavior if variables are functionally segregated (A) versus overlapping (B). (A) Functional segregation within a variable (e.g., body part) implies that a classifier trained to differentiate the levels of one dimension (e.g., right from left) will not generalize across the levels of the dimension of interest (e.g., from shoulder to hand) resulting in chance performance. (B) In contrast, functional overlap implies generalization resulting in above-chance performance when comparing classifier performance across levels.
(C) Performance of decoders trained on data split by body part for classifying the body side. Blue/orange bars represent the performance of the decoder trained on shoulder/hand movement data. Horizontal axis labels represent which body part’s data each decoder was tested on. Performance was measured as the fraction of trials accurately classified by the decoder, with in-sample performance determined by cross-validation. Asterisks represent performance significantly different from chance, as determined by a rank shuffle test. The red line represents chance performance level (0.5) while the green line represents perfect performance (1.0).
(D) Similar to (C) but decoding strategy instead of body side.
(E and F) Similar to (C) but with data split by body side and decoding for body part (E) and strategy (F), respectively.
(G and H) Similar to (C) but with data split by strategy and decoding for body side (G) and body part (H), respectively.
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9. Figure 8 All Movement Variables Decodable from the Population
Confusion matrix for cross-validated classification of the eight movement conditions (ALH, attempt left hand; ILH, imagine left hand; ARH, attempt right hand; IRH, imagine right hand; ALS, attempt left shoulder; ILS, imagine left shoulder; ARS, attempt right shoulder; IRS, imagine right shoulder).
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